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Development of an automated method of detecting stereotyped feeding events in multisensor data from tagged rorqual whales

The introduction of animal‐borne, multisensor tags has opened up many opportunities for ecological research, making previously inaccessible species and behaviors observable. The advancement of tag technology and the increasingly widespread use of bio‐logging tags are leading to large volumes of some...

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Autores principales: Allen, Ann N., Goldbogen, Jeremy A., Friedlaender, Ari S., Calambokidis, John
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5513260/
https://www.ncbi.nlm.nih.gov/pubmed/28725418
http://dx.doi.org/10.1002/ece3.2386
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author Allen, Ann N.
Goldbogen, Jeremy A.
Friedlaender, Ari S.
Calambokidis, John
author_facet Allen, Ann N.
Goldbogen, Jeremy A.
Friedlaender, Ari S.
Calambokidis, John
author_sort Allen, Ann N.
collection PubMed
description The introduction of animal‐borne, multisensor tags has opened up many opportunities for ecological research, making previously inaccessible species and behaviors observable. The advancement of tag technology and the increasingly widespread use of bio‐logging tags are leading to large volumes of sometimes extremely detailed data. With the increasing quantity and duration of tag deployments, a set of tools needs to be developed to aid in facilitating and standardizing the analysis of movement sensor data. Here, we developed an observation‐based decision tree method to detect feeding events in data from multisensor movement tags attached to fin whales (Balaenoptera physalus). Fin whales exhibit an energetically costly and kinematically complex foraging behavior called lunge feeding, an intermittent ram filtration mechanism. Using this automated system, we identified feeding lunges in 19 fin whales tagged with multisensor tags, during a total of over 100 h of continuously sampled data. Using movement sensor and hydrophone data, the automated lunge detector correctly identified an average of 92.8% of all lunges, with a false‐positive rate of 9.5%. The strong performance of our automated feeding detector demonstrates an effective, straightforward method of activity identification in animal‐borne movement tag data. Our method employs a detection algorithm that utilizes a hierarchy of simple thresholds based on knowledge of observed features of feeding behavior, a technique that is readily modifiable to fit a variety of species and behaviors. Using automated methods to detect behavioral events in tag records will significantly decrease data analysis time and aid in standardizing analysis methods, crucial objectives with the rapidly increasing quantity and variety of on‐animal tag data. Furthermore, our results have implications for next‐generation tag design, especially long‐term tags that can be outfitted with on‐board processing algorithms that automatically detect kinematic events and transmit ethograms via acoustic or satellite telemetry.
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spelling pubmed-55132602017-07-19 Development of an automated method of detecting stereotyped feeding events in multisensor data from tagged rorqual whales Allen, Ann N. Goldbogen, Jeremy A. Friedlaender, Ari S. Calambokidis, John Ecol Evol Original Research The introduction of animal‐borne, multisensor tags has opened up many opportunities for ecological research, making previously inaccessible species and behaviors observable. The advancement of tag technology and the increasingly widespread use of bio‐logging tags are leading to large volumes of sometimes extremely detailed data. With the increasing quantity and duration of tag deployments, a set of tools needs to be developed to aid in facilitating and standardizing the analysis of movement sensor data. Here, we developed an observation‐based decision tree method to detect feeding events in data from multisensor movement tags attached to fin whales (Balaenoptera physalus). Fin whales exhibit an energetically costly and kinematically complex foraging behavior called lunge feeding, an intermittent ram filtration mechanism. Using this automated system, we identified feeding lunges in 19 fin whales tagged with multisensor tags, during a total of over 100 h of continuously sampled data. Using movement sensor and hydrophone data, the automated lunge detector correctly identified an average of 92.8% of all lunges, with a false‐positive rate of 9.5%. The strong performance of our automated feeding detector demonstrates an effective, straightforward method of activity identification in animal‐borne movement tag data. Our method employs a detection algorithm that utilizes a hierarchy of simple thresholds based on knowledge of observed features of feeding behavior, a technique that is readily modifiable to fit a variety of species and behaviors. Using automated methods to detect behavioral events in tag records will significantly decrease data analysis time and aid in standardizing analysis methods, crucial objectives with the rapidly increasing quantity and variety of on‐animal tag data. Furthermore, our results have implications for next‐generation tag design, especially long‐term tags that can be outfitted with on‐board processing algorithms that automatically detect kinematic events and transmit ethograms via acoustic or satellite telemetry. John Wiley and Sons Inc. 2016-09-29 /pmc/articles/PMC5513260/ /pubmed/28725418 http://dx.doi.org/10.1002/ece3.2386 Text en © 2016 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd. This is an open access article under the terms of the Creative Commons Attribution (http://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Original Research
Allen, Ann N.
Goldbogen, Jeremy A.
Friedlaender, Ari S.
Calambokidis, John
Development of an automated method of detecting stereotyped feeding events in multisensor data from tagged rorqual whales
title Development of an automated method of detecting stereotyped feeding events in multisensor data from tagged rorqual whales
title_full Development of an automated method of detecting stereotyped feeding events in multisensor data from tagged rorqual whales
title_fullStr Development of an automated method of detecting stereotyped feeding events in multisensor data from tagged rorqual whales
title_full_unstemmed Development of an automated method of detecting stereotyped feeding events in multisensor data from tagged rorqual whales
title_short Development of an automated method of detecting stereotyped feeding events in multisensor data from tagged rorqual whales
title_sort development of an automated method of detecting stereotyped feeding events in multisensor data from tagged rorqual whales
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5513260/
https://www.ncbi.nlm.nih.gov/pubmed/28725418
http://dx.doi.org/10.1002/ece3.2386
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